Skip to contents

Produces estimates of total forest area (acreage) from FIA data. Estimates can be produced for regions defined within the FIA Database (e.g. counties), at the plot level, or within user-defined areal units. Options to group estimates by variables defined in the FIADB. If multiple reporting years (EVALIDs) are included in the data, estimates will be output as a time series. If multiple states are represented by the data, estimates will be output for the full region (all area combined), unless specified otherwise (e.g. grpBy = STATECD).

Usage

area(db, grpBy = NULL, polys = NULL, returnSpatial = FALSE,
     byLandType = FALSE, landType = 'forest',  method = 'TI',
     lambda = 0.5, treeDomain = NULL, areaDomain = NULL,
     totals = TRUE, variance = FALSE, byPlot = FALSE,
     condList = FALSE, nCores = 1)

Arguments

db

FIA.Database or Remote.FIA.Database object produced from readFIA() or getFIA(). If a Remote.FIA.Database, data will be read in and processed state-by-state to conserve RAM (see details for an example).

grpBy

variables from PLOT, COND, or TREE tables to group estimates by (NOT quoted). Multiple grouping variables should be combined with c(), and grouping will occur heirarchically. For example, to produce seperate estimates for each ownership group within methods of stand regeneration, specify c(STDORGCD, OWNGRPCD).

polys

sp or sf Polygon/MultiPolgyon object; Areal units to bin data for estimation. Separate estimates will be produced for regions encompassed by each areal unit. FIA plot locations will be reprojected to match projection of polys object.

returnSpatial

logical; if TRUE, merge population estimates with polys and return as sf multipolygon object. When byPlot = TRUE, return plot-level estimates as sf spatial points.

byLandType

logical; if TRUE, return estimates grouped by individual land type classes ("timberland", "non-timberland forest", "non-forest", and "water").

landType

character, one of: "forest", "timber", "non-forest", "water", or "all"; Type of land that estimates will be produced for. Timberland is a subset of forestland (default) which has high site potential and non-reserve status (see details).

method

character; design-based estimator to use. One of: "TI" (temporally indifferent, default), "annual" (annual), "SMA" (simple moving average), "LMA" (linear moving average), or "EMA" (exponential moving average). See Stanke et al 2020 for a complete description of these estimators.

lambda

numeric (0,1); if method = 'EMA', the decay parameter used to define weighting scheme for annual panels. Low values place higher weight on more recent panels, and vice versa. Specify a vector of values to compute estimates using mulitple wieghting schemes, and use plotFIA with grp set to lambda to produce moving average ribbon plots. See Stanke et al 2020 for examples.

treeDomain

logical predicates defined in terms of the variables in PLOT, TREE, and/or COND tables. Used to define the type of trees for which estimates will be produced (e.g. DBH greater than 20 inches: DIA > 20, Dominant/Co-dominant crowns only: CCLCD %in% 2:3. Multiple conditions are combined with & (and) or | (or). Only plots with at least one tree where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.

areaDomain

logical predicates defined in terms of the variables in PLOT and/or COND tables. Used to define the area for which estimates will be produced (e.g. within 1 mile of improved road: RDDISTCD %in% 1:6, Hard maple/basswood forest type: FORTYPCD == 805. Multiple conditions are combined with & (and) or | (or). Only plots within areas where the condition evaluates to TRUE are used in producing estimates. Should NOT be quoted.

totals

logical; if TRUE, return total population estimates (e.g. total area) along with ratio estimates (e.g. mean trees per acre).

variance

logical; if TRUE, return estimated variance (VAR) and sample size (N). If FALSE, return 'sampling error' (SE) as returned by EVALIDator. Note: sampling error cannot be used to construct confidence intervals.

byPlot

logical; if TRUE, returns estimates for individual plot locations instead of population estimates.

condList

logical; if TRUE, returns condition-level summaries intended for subsequent use with customPSE().

nCores

numeric; number of cores to use for parallel implementation. Check available cores using detectCores. Default = 1, serial processing.

Details

Estimation Details

Estimation of forest variables follows the procedures documented in Bechtold and Patterson (2005) and Stanke et al 2020. Area percentages in the domain of interest are represented as the total number of forest plots containing trees of a particular type (live, white pine) / total number of forest plots within the region and area domain. The total populations (e.g., the denominator of the ratio estimate) used to compute these percentages will not change by changing treeDomain. Instead, specifying treeDomain will change the specific plots used to determine the total area (e.g., the numerator of the ratio estimate) that meets the given tree requirements. The total population will change if the user specifies an areaDomain or if changing landType.

Note that when using grpBy with a tree-level parameter (e.g., SPCD), the total area across all groups will NOT be equal to the total area of the given land type. This is because the groups are not mutually exclusive (e.g., a plot can contain more than one species and thus be counted in the area calculations for multiple SPCDs). If specifying grpBy with a PLOT or COND level variable (e.g., forest type [FORTYPCD]), the groups are mutually exclusive and the area across all groups will sum to the total area of the given land type. When specifying grpBy or treeDomain, percentages are calculated relative to the total amount of land area specified by landType. For example, if setting treeDomain = SPCD == 121 and landType = 'forest', the area percentage returned will represent the estimated percentage of forest land that contains at least one longleaf pine (SPCD == 121) tree.

Users may specify alternatives to the 'Temporally Indifferent' estimator using the method argument. Alternative design-based estimators include the annual estimator ("ANNUAL"; annual panels, or estimates from plots measured in the same year), simple moving average ("SMA"; combines annual panels with equal weight), linear moving average ("LMA"; combine annual panels with weights that decay linearly with time since measurement), and exponential moving average ("EMA"; combine annual panels with weights that decay exponentially with time since measurement). The "best" estimator depends entirely on user-objectives, see Stanke et al 2020 for a complete description of these estimators and tradeoffs between precision and temporal specificity.

When byPlot = FALSE (i.e., population estimates are returned), the "YEAR" column in the resulting dataframe indicates the final year of the inventory cycle that estimates are produced for. For example, an estimate of current forest area (e.g., 2018) may draw on data collected from 2008-2018, and "YEAR" will be listed as 2018 (consistent with EVALIDator). However, when byPlot = TRUE (i.e., plot-level estimates returned), the "YEAR" column denotes the year that each plot was measured (MEASYEAR), which may differ slightly from its associated inventory year (INVYR).

Stratified random sampling techniques are most often employed to compute estimates in recent inventories, although double sampling and simple random sampling may be employed for early inventories. Estimates are adjusted for non-response bias by assuming attributes of non-response plot locations to be equal to the mean of other plots included within thier respective stratum or population.

Working with "Big Data"

If FIA data are too large to hold in memory (e.g., R throws the "cannot allocate vector of size ..." errors), use larger-than-RAM options. See documentation of readFIA() for examples of how to set up a Remote.FIA.Database. As a reference, we have used rFIA's larger-than-RAM methods to estimate forest variables using the entire FIA Database (~50GB) on a standard desktop computer with 16GB of RAM. Check out our website for more details and examples.

Easy, efficient parallelization is implemented with the parallel package. Users must only specify the nCores argument with a value greater than 1 in order to implement parallel processing on their machines. Parallel implementation is achieved using a snow type cluster on any Windows OS, and with multicore forking on any Unix OS (Linux, Mac). Implementing parallel processing may substantially decrease free memory during processing, particularly on Windows OS. Thus, users should be cautious when running in parallel, and consider implementing serial processing for this task if computational resources are limited (nCores = 1).

Definition of forestland

Forest land must have at least 10-percent canopy cover by live tally trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. Forest land includes transition zones, such as areas between heavily forest and non-forested lands that meet the mimium tree canopy cover and forest areas adjacent to urban and built-up lands. The minimum area for classification of forest land is 1 acre in size and 120 feet wide measured stem-to-stem from the outer-most edge. Roadside, streamside, and shelterbelt strips of trees must have a width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings, such as city parks, are not considered forest land.

Timber land is a subset of forest land that is producing or is capable of producing crops of industrial wood and not withdrawn from timber utilization by statute or administrative regulation. (Note: Areas qualifying as timberland are capable of producing at least 20 cubic feet per acre per year of industrial wood in natural stands. Currently inaccessible and inoperable areas are NOT included).

Value

Dataframe or sf object (if returnSpatial = TRUE). If byPlot = TRUE, values are returned for each plot (proportion of plot in domain of interest; PLOT_STATUS_CD = 1 when forest exists at the plot location). All variables with names ending in SE, represent the estimate of sampling error (%) of the variable. When variance = TRUE, variables ending in VAR denote the variance of the variable and N is the total sample size (i.e., including non-zero plots).

  • YEAR: reporting year associated with estimates

  • PERC_AREA: percent of area within the domain of interest

  • AREA_TOTAL: estimate of total area within domain of interest (acres)

  • nPlots_AREA_NUM: number of non-zero plots used to compute land area estimates within the domain of interest

  • nPlots_AREA_DEN: number of non-zero plots used to compute land area estimates

References

rFIA website: https://rfia.netlify.app/

FIA Database User Guide: https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi

Bechtold, W.A.; Patterson, P.L., eds. 2005. The Enhanced Forest Inventory and Analysis Program - National Sampling Design and Estimation Procedures. Gen. Tech. Rep. SRS - 80. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 85 p. https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs080/gtr_srs080.pdf

Stanke, H., Finley, A. O., Weed, A. S., Walters, B. F., & Domke, G. M. (2020). rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database. Environmental Modelling & Software, 127, 104664.

Author

Hunter Stanke, Andrew Finley, Jeffrey W. Doser

Note

All sampling error estimates (SE) are returned as the "percent coefficient of variation" (standard deviation / mean * 100) for consistency with EVALIDator. IMPORTANT: sampling error cannot be used to construct confidence intervals. Please use variance = TRUE for that (i.e., return variance and sample size instead of sampling error).

Examples

# Load data from the rFIA package
data(fiaRI)
data(countiesRI)

# Most recents subset
fiaRI_mr <- clipFIA(fiaRI)

# Most recent estimates of forested area in RI
area(db = fiaRI_mr)
#> # A tibble: 1 × 8
#>    YEAR PERC_AREA AREA_TOTAL PERC_AREA_SE AREA_TOTAL_SE nPlots_AREA_NUM
#>   <dbl>     <dbl>      <dbl>        <dbl>         <dbl>           <int>
#> 1  2018       100    366959.            0          3.53             127
#> # ℹ 2 more variables: nPlots_AREA_DEN <int>, N <int>

# \donttest{
# Same as above grouped by land class
area(db = fiaRI_mr, byLandType = TRUE)
#> # A tibble: 4 × 9
#>    YEAR landType PERC_AREA AREA_TOTAL PERC_AREA_SE AREA_TOTAL_SE nPlots_AREA_NUM
#>   <dbl> <chr>        <dbl>      <dbl>        <dbl>         <dbl>           <int>
#> 1  2018 Non-For…    18.3       82338.        11.8          13.5               48
#> 2  2018 Non-Tim…     3.95      17814.        36.6          36.6                7
#> 3  2018 Timber      77.4      349145.         3.25          4.03             121
#> 4  2018 Water        0.383      1728.        68.7          69.0                3
#> # ℹ 2 more variables: nPlots_AREA_DEN <int>, N <int>

# Estimates for area where stems greater than 20 in DBH occur for
# available inventories (time-series)
area(db = fiaRI,
     landType = 'forest',
     treeDomain = DIA > 20)
#> # A tibble: 6 × 8
#>    YEAR PERC_AREA AREA_TOTAL PERC_AREA_SE AREA_TOTAL_SE nPlots_AREA_NUM
#>   <dbl>     <dbl>      <dbl>        <dbl>         <dbl>           <int>
#> 1  2013      41.7    153019.        11.1          11.5               47
#> 2  2014      44.6    163922.        10.5          11.1               49
#> 3  2015      46.2    170891.        10.1          10.7               51
#> 4  2016      48.8    178701.         9.50         10.1               54
#> 5  2017      52.0    191500.         8.82          9.46              58
#> 6  2018      52.7    193487.         8.59          9.25              60
#> # ℹ 2 more variables: nPlots_AREA_DEN <int>, N <int>

# Same as above, but implemented in parallel (much quicker)
parallel::detectCores(logical = FALSE) # 4 cores available, we will take 2
#> [1] 16
area(db = fiaRI,
     landType = 'forest',
     treeDomain = DIA > 20,
     nCores =2)
#> # A tibble: 6 × 8
#>    YEAR PERC_AREA AREA_TOTAL PERC_AREA_SE AREA_TOTAL_SE nPlots_AREA_NUM
#>   <dbl>     <dbl>      <dbl>        <dbl>         <dbl>           <int>
#> 1  2013      41.7    153019.        11.1          11.5               47
#> 2  2014      44.6    163922.        10.5          11.1               49
#> 3  2015      46.2    170891.        10.1          10.7               51
#> 4  2016      48.8    178701.         9.50         10.1               54
#> 5  2017      52.0    191500.         8.82          9.46              58
#> 6  2018      52.7    193487.         8.59          9.25              60
#> # ℹ 2 more variables: nPlots_AREA_DEN <int>, N <int>

# Return estimates at the plot-level
area(db = fiaRI,
     byPlot = TRUE)
#> # A tibble: 215 × 5
#>     YEAR pltID      PLOT_STATUS_CD  PLT_CN PROP_FOREST
#>    <int> <chr>               <int>   <dbl>       <dbl>
#>  1  2009 1_44_3_129              1 1.45e14       1    
#>  2  2009 1_44_3_135              1 1.45e14       1    
#>  3  2009 1_44_3_52               1 1.45e14       1    
#>  4  2009 1_44_7_113              1 1.45e14       1    
#>  5  2009 1_44_7_156              1 1.45e14       0.913
#>  6  2009 1_44_7_194              1 1.45e14       0.341
#>  7  2009 1_44_7_218              1 1.45e14       0.75 
#>  8  2009 1_44_7_254              1 1.45e14       0.931
#>  9  2009 1_44_7_276              1 1.45e14       0.530
#> 10  2009 1_44_7_283              1 1.45e14       0.227
#> # ℹ 205 more rows
  # }